In video coding systems, a conventional encoder may code a source video sequence into a coded representation that has a smaller bit rate than does the source video and, thereby achieve data compression. A decoder may then invert the coding processes performed by the encoder to retrieve the source video.
To improve the quality of a video signal, the signal may be filtered to reduce noise. The signal may be both spatially and temporally filtered. Spatial filters reduce or filter noise in a single captured frame by averaging the signals representing the captured frame to cut off outlying signals that likely represent noise. The spatial filter is conventionally implemented with a low pass filter. Temporal filters reduce or filter noise across multiple frames representing images captured over time. Then, for frames depicting the same scene, the difference between the frames may represent noise. The temporal filter identifies these differences and reduces the identified noise throughout the sequence of frames. Then the SNR will improve over time as the spatial filter uses the scene history to improve filtering operations. A noticeable artifact in an image may be the result of strong temporal based filtering causing ghosting, the result of motion blur, or may just be distortion caused by noise.
Some camera capture processes may result in noise of a specific profile. For example, in low light, a captured image may have a specific noise profile that is manifested in noise having a particular variance, color, saturation, brightness, color-shift, or other identifiable characteristics. However, this noise may be difficult to remove with traditional filters as noise of specific colors may be identified as objects in the image or objects may be identified as noise.
Accordingly, there is a need in the art for systems and methods to identify and minimize noise in images captured during disadvantageous lighting conditions.